arXiv research

Adaptive time decay functions improve financial product recommendation accuracy.

Starting from the generalized exponential function $\exp_κ(x)=(\sqrt{1+κ^{2}x^{2}}+κx)^{1/κ}$, with $\exp_{0}(x)=\exp(x)$, proposed in Ref. [G. Kaniadakis, Physica A \textbf{296}, 405 (2001)], the survival function $P_{>}(x)=\exp_κ(-βx^α)$, where $x\in\mathbf{R}^{+}$, $α,β>0$, and $κ\in[0,1)$, is considered in order to…

With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications. However, most existing works in crowdsourcing mainly focus on label inference and incentive design. In this paper, we address a different problem of adaptive crowd teaching, which is a …

Though used extensively, the concept and process of machine learning (ML) personalization have generally received little attention from academics, practitioners, and the general public. We describe the ML approach as relying on the metaphor of the person as a feature vector and contrast this with humanistic views of th…

Adaptive personalized federated learning improves local model personalization.

Framework generates personalized insulin treatment strategies using deep models.

Spaced repetition is among the most studied learning strategies in the cognitive science literature. It consists in temporally distributing exposure to an information so as to improve long-term memorization. Providing students with an adaptive and personalized distributed practice schedule would benefit more than just …

FMP improves personalized ASR models on private devices.

Federated learning is a distributed, on-device computation framework that enables training global models without exporting sensitive user data to servers. In this work, we describe methods to extend the federation framework to evaluate strategies for personalization of global models. We present tools to analyze the eff…

Generative framework unifies and improves personalized learning and estimation methods.

A new federated multi-armed bandit framework with personalization balances generalization and personalization.

The Myers-Briggs Type Indicator (MBTI) is a popular personality metric that uses four dichotomies as indicators of personality traits. This paper examines the use of pre-trained language models to predict MBTI personality types based on scraped labeled texts. The proposed model reaches an accuracy of $0.47$ for correct…

Unified proof for scalable personalized federated learning.

Explanation in machine learning and related fields such as artificial intelligence aims at making machine learning models and their decisions understandable to humans. Existing work suggests that personalizing explanations might help to improve understandability. In this work, we derive a conceptualization of personali…

Existing Recommender Systems mainly focus on exploiting users' feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-base…

This paper analyzes the trade-off between accuracy and communication in personalized federated learning.

Personalized sleep staging achieved with single-night data using KL-divergence regularization.

System interprets complex treatment effects for personalized policies.

FSPO optimizes synthetic preferences for LLM personalization.

Perceived personality traits attributed to an individual do not have to correspond to their actual personality traits and may be determined in part by the context in which one encounters a person. These apparent traits determine, to a large extent, how other people will behave towards them. Deep neural networks are inc…

The paper analyzes a game where players must balance short-term and long-term interests, leading to cooperative or competitive outcomes.

Group personalization improves FL performance in heterogeneous client data.

LP-FT improves personalized model training in FL by balancing generalization and personalization.

Study assesses whether RL algorithm personalizes treatment sequences.

Extends Minkowski stability proof to minimal decay assumptions.

New method creates vacuum data at minimal and borderline decay thresholds.

The paper explores fairness, welfare, and equity in personalized pricing across various applications.

pFedMe uses Moreau envelopes to improve personalized FL performance.

We discuss training techniques, objectives and metrics toward personalization of deep learning models. In machine learning, personalization addresses the goal of a trained model to target a particular individual by optimizing one or more performance metrics, while conforming to certain constraints. To personalize, we i…

A new Federated Learning approach balances personalization and global training.

Personalizes query completion in digital commerce with image-based vectors.

Unique solutions found for wave-like decaying null infinity equations.

Statistical test evaluates if personalizing interventions is cost-effective.

PerSense assesses personality traits from text for commonsense reasoning.

This paper surveys techniques to personalize federated learning models.

The paper tackles carousel personalization in music streaming apps using contextual bandits.

A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recomme…

This paper improves federated learning by selecting between global and personalized models based on uncertainty.

New findings on flatness of certain metrics with fast decay.

Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thoroughly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem.…

Study on curvature decay in steady Ricci solitons, proving dichotomy.

Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness. Most previous work applies gaits extracted from WiFi distortions caused by the person walking to achieve the identification. However, to extract useful gait, a p…

PCL tackles collaborative learning for diverse agents, reducing sample complexity.

Personalized models using group attributes reduce performance, study finds.

Paper develops privacy-preserving dynamic pricing policy for e-commerce.

Cautious Weight Decay modifies weight decay for better optimization.

Boosting-inspired framework for federated learning with progressive model personalization.

Study on scalar curvature decay on non-compact manifolds linked at infinity.